Artificial Intelligence

Slides:



Advertisements
Similar presentations
CS344: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 10– Club and Circuit Examples.
Advertisements

CSE 311: Foundations of Computing Fall 2013 Lecture 3: Logic and Boolean algebra.
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 9,10,11- Logic; Deduction Theorem 23/1/09 to 30/1/09.
CS344: Artificial Intelligence
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–5 and 6: Propositional Calculus.
Propositional Logic Discrete Structures (CS 173) Madhusudan Parthasarathy, University of Illinois School of Athens Fresco by Raphael Wikimedia Commons.
CS344: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 7– Predicate Calculus and Knowledge Representation.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–28, 29: Predicate Calculus;
What is Science?.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 21– Predicate Calculus and Knowledge Representation 7 th September,
Logical and Rule-Based Reasoning Part I. Logical Models and Reasoning Big Question: Do people think logically?
Adapted from Discrete Math
CS621: Artificial Intelligence Lecture 27: Backpropagation applied to recognition problems; start of logic Pushpak Bhattacharyya Computer Science and Engineering.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 17, 18: Predicate Calculus 15.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 15, 16: Predicate Calculus 8.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–4: Fuzzy Control of Inverted.
CS6133 Software Specification and Verification
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–7: (a) Formal System;(b) How to read research papers 3 rd August, 2010.
KNOWLEDGE BASED SYSTEMS
Artificial Intelligence 7. Making Deductive Inferences Course V231 Department of Computing Imperial College, London Jeremy Gow.
CS621: Artificial Intelligence Lecture 28: Propositional calculus; Puzzle Solving Pushpak Bhattacharyya Computer Science and Engineering Department IIT.
Chapter 1: The Foundations: Logic and Proofs
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 9- Completeness proof; introducing knowledge representation.
Predicates and Quantifiers Dr. Yasir Ali. 1.Predicates 2.Quantifiers a.Universal Quantifiers b.Existential Quantifiers 3.Negation of Quantifiers 4.Universal.
11 Artificial Intelligence CS 165A Thursday, October 25, 2007  Knowledge and reasoning (Ch 7) Propositional logic 1.
ARTIFICIAL INTELLIGENCE Lecture 2 Propositional Calculus.
CS 344 Artificial Intelligence By Prof: Pushpak Bhattacharya Class on 12/Feb/2007.
CS 344 Artificial Intelligence By Prof: Pushpak Bhattacharya Class on 26/Feb/2007.
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 4- Logic.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–6: Propositional calculus, Semantic Tableau, formal System 2 nd August,
ECE457 Applied Artificial Intelligence Fall 2007 Lecture #6
CS621: Artificial Intelligence
Chapter 7. Propositional and Predicate Logic
2. The Logic of Compound Statements Summary
Chapter 8 : Fuzzy Logic.
CS344: Introduction to Artificial Intelligence (associated lab: CS386)
Advanced Algorithms Analysis and Design
Knowledge Representation and Reasoning
CSE 311 Foundations of Computing I
CS 621 Artificial Intelligence Lecture 1 – 28/07/05
Proposition & Predicates
Knowledge Acquisition
CS 621 Artificial Intelligence Lecture 4 – 05/08/05
CSE 311 Foundations of Computing I
Logical Inferences: A set of premises accompanied by a suggested conclusion regardless of whether or not the conclusion is a logical consequence of the.
CS201: Data Structures and Discrete Mathematics I
CS 270 Math Foundations of CS
Propositional Equivalences
CSE 311 Foundations of Computing I
CS 220: Discrete Structures and their Applications
Applied Discrete Mathematics Week 1: Logic
CS 416 Artificial Intelligence
Back to “Serious” Topics…
Computer Security: Art and Science, 2nd Edition
CS621: Artificial Intelligence
Chapter 7. Propositional and Predicate Logic
CSNB234 ARTIFICIAL INTELLIGENCE
Methods of Proof Chapter 7, second half.
CS621: Artificial Intelligence
Artificial Intelligence
ece 720 intelligent web: ontology and beyond
CS621: Artificial Intelligence
Artificial Intelligence
CS344 : Introduction to Artificial Intelligence
Logical and Rule-Based Reasoning Part I
CS621: Artificial Intelligence
CS201: Data Structures and Discrete Mathematics I
CS 621 Artificial Intelligence Lecture /09/05 Prof
Artificial Intelligence
Habib Ullah qamar Mscs(se)
Presentation transcript:

Artificial Intelligence CS 344 Artificial Intelligence By Prof: Pushpak Bhattacharya Class on 25/Jan/2007

Logic and inferencing Vision NLP Search Reasoning Learning Knowledge Robotics Expert Systems Planning Obtaining implication of given facts and rules -- Hallmark of intelligence

Inferencing through Deduction Induction Deduction (General to specific) Induction (Specific to General) Abduction (Conclusion to hypothesis in absence of any other evidence to contrary) Deduction Given: All men are mortal (rule) Shakespeare is a man (fact) To prove: Shakespeare is mortal (inference) Induction Given: Shakespeare is mortal Newton is mortal (Observation) Dijkstra is mortal To prove: All men are mortal (Generalization)

If there is rain, then there will be no picnic Fact1: There was rain Conclude: There was no picnic Deduction Fact2: There was no picnic Conclude: There was no rain (?) Induction and abduction are fallible forms of reasoning. Their conclusions are susceptible to retraction Two systems of logic 1) Propositional calculus 2) Predicate calculus

Propositions Stand for facts/assertions Declarative statements As opposed to interrogative statements (questions) or imperative statements (request, order) Operators => and ¬ form a minimal set (can express other operations) - Prove it. Tautologies are formulae whose truth value is always T, whatever the assignment is

Model In propositional calculus any formula with n propositions has 2n models (assignments) - Tautologies evaluate to T in all models. Examples: 1) 2) - De Morgan with AND

Semantic Tree/Tableau method of proving tautology To prove: Start with the negation of the formula to be proved a tautology α-formula β-formula - β - formula α-formula - α - formula

Example 2: Contradictions in all paths X (α - formula) (α - formulae) B C Contradictions in all paths B C

Exercise: Prove the backward implication in the previous example